1. Field of Invention
The present invention relates to a method for designing health-monitoring systems based upon an optimized embedded kernel for performing FDI&P in CBM and RTM systems. In this invention the Embedded Optimized Neuro Genetic Fast Estimator (ONGFE) instantiates ISEs, which are tailored for conducting FDI&P. Each ISE monitors a system component status. Then health monitoring at the system level is performed by linking ISEs. Distinctive characteristics of the embedded kernel and computational hardware platform are: fast learning; ANN optimization by novel pseudogenetic algorithm; on-line learning; methods for synchronization and communication with secondary diagnostic modules; highly distributed software and hardware architecture; real time operation; and embedded sensor data validation algorithm.
2. Description of Related Arts
Health monitoring (HM) refers to a set of techniques (algorithms) and their implementation, which are used for tracking system operational condition. They aim to achieve high reliability, availability, safety, and maintainability by conducting failure detection and prognostics. Based on this kind of technology maintenance practices, real time monitoring, and depot operation can be enhanced and automated for reducing cost, keeping critical assets in operation, avoiding catastrophic failures, supporting efficient maintenance practices, and managing resources. A particular example is its use for implementation of CBM systems where scheduled maintenance practices are replaced by schemes where maintenance is driven based on the system operational condition instead. Many applications can be listed for exemplifying the technological impact of health monitoring systems. Examples include (among many others) CBM and RTM for: aircraft's engines, ships' turbines, airplane structures, actuators, and on ground vehicles' systems. In the last years, great effort has been conducted by different government agencies promoting research and development in HM, CBM, and RTM. For example, NASA has been strongly supporting the development of novel innovative technologies to detect failures in propulsion systems (engines), exploration platforms, and in different aerospace vehicles. By achieving efficient health monitoring capability then reliable CBM and RTM can be provided.